f893530471
🎯 Major improvements to MissionControl component: - Always keep input field visible and functional after AI responses - Auto-clear input after submitting questions for better UX - Add dynamic visual indicators (first question vs follow-up) - Improve response layout with clear separation and hints - Enable proper chat-like experience for continuous learning 🌟 Additional enhancements: - Better language-specific messaging throughout interface - Clearer visual hierarchy between input and response areas - Intuitive flow that guides users to ask follow-up questions - Maintains responsive design and accessibility 🔧 Technical changes: - Enhanced MissionControl state management - Improved component layout and styling - Better TypeScript integration across components - Updated tsconfig for stricter type checking
52 lines
2.2 KiB
JavaScript
52 lines
2.2 KiB
JavaScript
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
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import { APIResource } from "../resource.mjs";
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import * as Core from "../core.mjs";
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export class Embeddings extends APIResource {
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/**
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* Creates an embedding vector representing the input text.
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*
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* @example
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* ```ts
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* const createEmbeddingResponse =
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* await client.embeddings.create({
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* input: 'The quick brown fox jumped over the lazy dog',
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* model: 'text-embedding-3-small',
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* });
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* ```
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*/
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create(body, options) {
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const hasUserProvidedEncodingFormat = !!body.encoding_format;
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// No encoding_format specified, defaulting to base64 for performance reasons
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// See https://github.com/openai/openai-node/pull/1312
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let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
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if (hasUserProvidedEncodingFormat) {
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Core.debug('Request', 'User defined encoding_format:', body.encoding_format);
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}
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const response = this._client.post('/embeddings', {
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body: {
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...body,
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encoding_format: encoding_format,
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},
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...options,
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});
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// if the user specified an encoding_format, return the response as-is
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if (hasUserProvidedEncodingFormat) {
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return response;
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}
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// in this stage, we are sure the user did not specify an encoding_format
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// and we defaulted to base64 for performance reasons
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// we are sure then that the response is base64 encoded, let's decode it
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// the returned result will be a float32 array since this is OpenAI API's default encoding
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Core.debug('response', 'Decoding base64 embeddings to float32 array');
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return response._thenUnwrap((response) => {
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if (response && response.data) {
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response.data.forEach((embeddingBase64Obj) => {
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const embeddingBase64Str = embeddingBase64Obj.embedding;
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embeddingBase64Obj.embedding = Core.toFloat32Array(embeddingBase64Str);
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});
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}
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return response;
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});
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}
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}
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//# sourceMappingURL=embeddings.mjs.map
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